Overview

Dataset statistics

Number of variables11
Number of observations1100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory94.7 KiB
Average record size in memory88.1 B

Variable types

NUM9
CAT2

Warnings

Star ColorRGB 0-255 has a high cardinality: 379 distinct values High cardinality
Bolo MagMbol is highly correlated with Abs MagMvHigh correlation
Abs MagMv is highly correlated with Bolo MagMbolHigh correlation
RadiusRstar/Rsun is highly correlated with LuminosityLstar/LsunHigh correlation
LuminosityLstar/Lsun is highly correlated with RadiusRstar/RsunHigh correlation
df_index has unique values Unique
StellarType has unique values Unique

Reproduction

Analysis started2020-12-08 04:46:53.405469
Analysis finished2020-12-08 04:47:08.420853
Duration15.02 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct1100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean618.7072727
Minimum1
Maximum1235
Zeros0
Zeros (%)0.0%
Memory size8.6 KiB
2020-12-07T21:47:08.507645image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile61.95
Q1309.75
median618.5
Q3928.25
95-th percentile1175.05
Maximum1235
Range1234
Interquartile range (IQR)618.5

Descriptive statistics

Standard deviation357.3266268
Coefficient of variation (CV)0.5775374601
Kurtosis-1.200770317
Mean618.7072727
Median Absolute Deviation (MAD)309.5
Skewness-0.0006035148592
Sum680578
Variance127682.3182
MonotocityStrictly increasing
2020-12-07T21:47:08.677219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
123510.1%
 
41510.1%
 
40810.1%
 
40910.1%
 
41010.1%
 
41110.1%
 
41210.1%
 
41310.1%
 
41610.1%
 
38610.1%
 
Other values (1090)109099.1%
 
ValueCountFrequency (%) 
110.1%
 
210.1%
 
310.1%
 
410.1%
 
510.1%
 
ValueCountFrequency (%) 
123510.1%
 
123410.1%
 
123310.1%
 
123210.1%
 
123110.1%
 

Abs MagMv
Real number (ℝ)

HIGH CORRELATION

Distinct190
Distinct (%)17.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.213636364
Minimum-9.5
Maximum19
Zeros7
Zeros (%)0.6%
Memory size8.6 KiB
2020-12-07T21:47:08.846508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-9.5
5-th percentile-9.3
Q1-6.5
median-3.8
Q33.1
95-th percentile14.2
Maximum19
Range28.5
Interquartile range (IQR)9.6

Descriptive statistics

Standard deviation7.149003981
Coefficient of variation (CV)-5.890565078
Kurtosis0.2212808114
Mean-1.213636364
Median Absolute Deviation (MAD)3.7
Skewness1.054579862
Sum-1335
Variance51.10825792
MonotocityNot monotonic
2020-12-07T21:47:08.998496image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-9.3363.3%
 
-5.5343.1%
 
-7282.5%
 
3.2272.5%
 
-5.1252.3%
 
-6.5242.2%
 
-4.7222.0%
 
-2.5191.7%
 
3.3181.6%
 
-5.4171.5%
 
Other values (180)85077.3%
 
ValueCountFrequency (%) 
-9.570.6%
 
-9.4131.2%
 
-9.3363.3%
 
-9.2151.4%
 
-9.1151.4%
 
ValueCountFrequency (%) 
1980.7%
 
18.340.4%
 
17.940.4%
 
17.530.3%
 
16.880.7%
 

Bolo CorrBC(Temp)
Real number (ℝ)

Distinct136
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.465527273
Minimum-8.38
Maximum-0.08
Zeros0
Zeros (%)0.0%
Memory size8.6 KiB
2020-12-07T21:47:09.161835image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-8.38
5-th percentile-6.93
Q1-3.86
median-1.99
Q3-0.62
95-th percentile-0.1
Maximum-0.08
Range8.3
Interquartile range (IQR)3.24

Descriptive statistics

Standard deviation2.082540225
Coefficient of variation (CV)-0.8446632281
Kurtosis0.2721092652
Mean-2.465527273
Median Absolute Deviation (MAD)1.61
Skewness-0.8700099815
Sum-2712.08
Variance4.336973789
MonotocityNot monotonic
2020-12-07T21:47:09.320119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-1.65333.0%
 
-4.01302.7%
 
-0.09272.5%
 
-3.15242.2%
 
-4.58242.2%
 
-0.08211.9%
 
-8.38201.8%
 
-0.1191.7%
 
-4.8151.4%
 
-4.16151.4%
 
Other values (126)87279.3%
 
ValueCountFrequency (%) 
-8.38201.8%
 
-8.3121.1%
 
-7.5560.5%
 
-7.0290.8%
 
-6.93151.4%
 
ValueCountFrequency (%) 
-0.08211.9%
 
-0.09272.5%
 
-0.1191.7%
 
-0.11111.0%
 
-0.12100.9%
 

Bolo MagMbol
Real number (ℝ)

HIGH CORRELATION

Distinct582
Distinct (%)52.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.679163636
Minimum-17.38
Maximum15.18
Zeros0
Zeros (%)0.0%
Memory size8.6 KiB
2020-12-07T21:47:09.475172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-17.38
5-th percentile-12.484
Q1-9.655
median-5.85
Q31.6175
95-th percentile11.19
Maximum15.18
Range32.56
Interquartile range (IQR)11.2725

Descriptive statistics

Standard deviation7.531126422
Coefficient of variation (CV)-2.046966965
Kurtosis-0.4848662686
Mean-3.679163636
Median Absolute Deviation (MAD)4.575
Skewness0.7422590012
Sum-4047.08
Variance56.71786518
MonotocityNot monotonic
2020-12-07T21:47:09.621183image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-11.08121.1%
 
-9.9190.8%
 
10.780.7%
 
-8.8580.7%
 
-7.1570.6%
 
-9.5170.6%
 
6.1970.6%
 
8.7360.5%
 
12.6560.5%
 
14.760.5%
 
Other values (572)102493.1%
 
ValueCountFrequency (%) 
-17.3840.4%
 
-16.0330.3%
 
-15.3310.1%
 
-15.2840.4%
 
-14.8630.3%
 
ValueCountFrequency (%) 
15.1860.5%
 
14.760.5%
 
14.1160.5%
 
13.560.5%
 
12.9420.2%
 

Color IndexB-V
Real number (ℝ)

Distinct126
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7154363636
Minimum-0.37
Maximum2.4
Zeros8
Zeros (%)0.7%
Memory size8.6 KiB
2020-12-07T21:47:09.776558image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.37
5-th percentile-0.35
Q1-0.27
median0.72
Q31.43
95-th percentile2.25
Maximum2.4
Range2.77
Interquartile range (IQR)1.7

Descriptive statistics

Standard deviation0.8980828284
Coefficient of variation (CV)1.255293796
Kurtosis-1.355364519
Mean0.7154363636
Median Absolute Deviation (MAD)0.865
Skewness0.2216615765
Sum786.98
Variance0.8065527666
MonotocityNot monotonic
2020-12-07T21:47:09.932128image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-0.35787.1%
 
-0.34393.5%
 
-0.32393.5%
 
-0.33333.0%
 
1.49272.5%
 
1.41252.3%
 
-0.3242.2%
 
-0.31242.2%
 
2.4201.8%
 
-0.06161.5%
 
Other values (116)77570.5%
 
ValueCountFrequency (%) 
-0.3760.5%
 
-0.35787.1%
 
-0.34393.5%
 
-0.33333.0%
 
-0.32393.5%
 
ValueCountFrequency (%) 
2.4201.8%
 
2.39121.1%
 
2.2690.8%
 
2.25151.4%
 
2.1750.5%
 

LuminosityLstar/Lsun
Real number (ℝ≥0)

HIGH CORRELATION

Distinct639
Distinct (%)58.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4913954.586
Minimum6.73e-05
Maximum711000000
Zeros0
Zeros (%)0.0%
Memory size8.6 KiB
2020-12-07T21:47:10.111457image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum6.73e-05
5-th percentile0.00266
Q117.9
median17350
Q3576750
95-th percentile7838500
Maximum711000000
Range711000000
Interquartile range (IQR)576732.1

Descriptive statistics

Standard deviation44828400.87
Coefficient of variation (CV)9.122673009
Kurtosis223.4465663
Mean4913954.586
Median Absolute Deviation (MAD)17349.9994
Skewness14.55034464
Sum5405350045
Variance2.009585525e+15
MonotocityNot monotonic
2020-12-07T21:47:10.280369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
215000090.8%
 
0.0041580.7%
 
0.26560.5%
 
0.00010560.5%
 
0.00069360.5%
 
0.0018260.5%
 
6.73e-0560.5%
 
0.00031560.5%
 
6.9160.5%
 
43700060.5%
 
Other values (629)103594.1%
 
ValueCountFrequency (%) 
6.73e-0560.5%
 
0.00010560.5%
 
0.0001860.5%
 
0.00031560.5%
 
0.0005320.2%
 
ValueCountFrequency (%) 
71100000040.4%
 
20500000030.3%
 
10800000010.1%
 
10300000040.4%
 
6960000030.3%
 

MassMstar/Msun
Real number (ℝ≥0)

Distinct254
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.94218182
Minimum0.1
Maximum160
Zeros0
Zeros (%)0.0%
Memory size8.6 KiB
2020-12-07T21:47:10.638771image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.2
Q12.175
median7.6
Q315.8
95-th percentile130
Maximum160
Range159.9
Interquartile range (IQR)13.625

Descriptive statistics

Standard deviation41.29636978
Coefficient of variation (CV)1.655683937
Kurtosis2.710068057
Mean24.94218182
Median Absolute Deviation (MAD)5.95
Skewness2.007523769
Sum27436.4
Variance1705.390157
MonotocityNot monotonic
2020-12-07T21:47:11.056071image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.1353.2%
 
0.2272.5%
 
0.8211.9%
 
0.7211.9%
 
0.5201.8%
 
0.3181.6%
 
0.6181.6%
 
0.9171.5%
 
0.4141.3%
 
2.1141.3%
 
Other values (244)89581.4%
 
ValueCountFrequency (%) 
0.1353.2%
 
0.2272.5%
 
0.3181.6%
 
0.4141.3%
 
0.5201.8%
 
ValueCountFrequency (%) 
16030.3%
 
159.730.3%
 
159.430.3%
 
15930.3%
 
158.730.3%
 

RadiusRstar/Rsun
Real number (ℝ≥0)

HIGH CORRELATION

Distinct594
Distinct (%)54.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2701.506317
Minimum0.00696
Maximum231000
Zeros0
Zeros (%)0.0%
Memory size8.6 KiB
2020-12-07T21:47:11.285324image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.00696
5-th percentile0.00944
Q13.0375
median20.5
Q3235.5
95-th percentile7567
Maximum231000
Range230999.993
Interquartile range (IQR)232.4625

Descriptive statistics

Standard deviation16541.59291
Coefficient of variation (CV)6.123099844
Kurtosis136.6412544
Mean2701.506317
Median Absolute Deviation (MAD)20.117
Skewness10.89935991
Sum2971656.948
Variance273624295.8
MonotocityNot monotonic
2020-12-07T21:47:11.439901image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.00864121.1%
 
20.2111.0%
 
0.55390.8%
 
1590.8%
 
14.990.8%
 
19.890.8%
 
20.570.6%
 
0.0069660.5%
 
12.560.5%
 
14.260.5%
 
Other values (584)101692.4%
 
ValueCountFrequency (%) 
0.0069660.5%
 
0.00864121.1%
 
0.0088760.5%
 
0.0088960.5%
 
0.008960.5%
 
ValueCountFrequency (%) 
23100040.4%
 
10300030.3%
 
8790040.4%
 
6820010.1%
 
5100030.3%
 

Star ColorRGB 0-255
Categorical

HIGH CARDINALITY

Distinct379
Distinct (%)34.5%
Missing0
Missing (%)0.0%
Memory size8.6 KiB
144 166 255
 
24
148 170 255
 
21
146 168 255
 
21
255 157 000
 
21
153 174 255
 
15
Other values (374)
998 
ValueCountFrequency (%) 
144 166 255242.2%
 
148 170 255211.9%
 
146 168 255211.9%
 
255 157 000211.9%
 
153 174 255151.4%
 
155 176 255151.4%
 
151 172 255151.4%
 
157 177 255121.1%
 
255 204 143121.1%
 
157 178 255121.1%
 
Other values (369)93284.7%
 
2020-12-07T21:47:11.626867image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique152 ?
Unique (%)13.8%
2020-12-07T21:47:12.116519image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length11
Median length11
Mean length11
Min length11

StellarType
Categorical

UNIQUE

Distinct1100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size8.6 KiB
N5Ia0
 
1
WC2III
 
1
M4VI
 
1
B9II
 
1
G3VI
 
1
Other values (1095)
1095 
ValueCountFrequency (%) 
N5Ia010.1%
 
WC2III10.1%
 
M4VI10.1%
 
B9II10.1%
 
G3VI10.1%
 
DO510.1%
 
M8Ia10.1%
 
WC4Ia010.1%
 
G7Ia010.1%
 
A3VI10.1%
 
Other values (1090)109099.1%
 
2020-12-07T21:47:12.252547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1100 ?
Unique (%)100.0%
2020-12-07T21:47:12.404156image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length4
Mean length4.209090909
Min length3

TempK
Real number (ℝ≥0)

Distinct171
Distinct (%)15.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14385.17545
Minimum1990
Maximum100000
Zeros0
Zeros (%)0.0%
Memory size8.6 KiB
2020-12-07T21:47:12.539703image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1990
5-th percentile2180
Q13640
median5670
Q324380
95-th percentile47600
Maximum100000
Range98010
Interquartile range (IQR)20740

Descriptive statistics

Standard deviation16380.70046
Coefficient of variation (CV)1.138720936
Kurtosis3.104995851
Mean14385.17545
Median Absolute Deviation (MAD)2879
Skewness1.660563497
Sum15823693
Variance268327347.6
MonotocityNot monotonic
2020-12-07T21:47:12.697093image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3700252.3%
 
30200242.2%
 
50000242.2%
 
1990201.8%
 
36800151.4%
 
41200151.4%
 
45600151.4%
 
4669151.4%
 
2180151.4%
 
34600151.4%
 
Other values (161)91783.4%
 
ValueCountFrequency (%) 
1990201.8%
 
2000121.1%
 
216790.8%
 
2180151.4%
 
228850.5%
 
ValueCountFrequency (%) 
10000060.5%
 
5040060.5%
 
50000242.2%
 
47800151.4%
 
4760090.8%
 

Interactions

2020-12-07T21:46:54.842170image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:46:55.016848image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:46:55.165448image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:46:55.317854image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:46:55.470165image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:46:55.618930image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:46:55.783102image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:46:55.946089image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:46:56.095834image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:46:56.244738image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:46:56.401728image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:46:56.562093image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:46:56.717676image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:46:56.878582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:46:57.039808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:46:57.205103image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:46:57.367944image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:46:57.521256image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:46:57.671700image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:46:57.824172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:46:57.996271image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:46:58.143148image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:46:58.322320image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:46:58.467765image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:46:58.618787image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:46:58.791570image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:46:58.929865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:46:59.061866image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:46:59.217908image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:46:59.370694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:46:59.517557image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:46:59.673963image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:46:59.827698image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:00.018142image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:00.303054image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:00.470613image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:00.611399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:00.754974image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:00.901234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:01.039188image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:01.184582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:01.341559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:01.497309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:01.656688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:01.817939image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:01.974987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:02.149460image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:02.317717image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:02.489038image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:02.755159image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:03.019333image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:03.236362image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:03.395370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:03.539089image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:03.690585image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:04.164030image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:04.322911image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:04.469958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:04.623086image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:04.778756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:04.943460image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:05.102847image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:05.250196image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:05.400703image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:05.534696image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:05.667658image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:05.796198image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:05.931303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:06.066728image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:06.205712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:06.349494image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:06.478985image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:06.608244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:06.747488image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:06.885978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:07.018906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:07.159034image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:07.299104image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:07.442245image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:07.587393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:07.720751image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-12-07T21:47:12.848075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-07T21:47:13.077532image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-07T21:47:13.300080image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-07T21:47:13.521752image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-12-07T21:47:08.006977image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-07T21:47:08.296473image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

df_indexAbs MagMvBolo CorrBC(Temp)Bolo MagMbolColor IndexB-VLuminosityLstar/LsunMassMstar/MsunRadiusRstar/RsunStar ColorRGB 0-255StellarTypeTempK
01-9.5-4.58-14.08-0.3534100000.0160.080.2144 166 255O0Ia050000.0
12-6.7-4.58-11.28-0.352590000.0150.022.1144 166 255O0Ia50000.0
23-6.5-4.58-11.08-0.352150000.0140.020.2144 166 255O0Ib50000.0
34-6.5-4.58-11.08-0.352150000.0130.020.2144 166 255O0II50000.0
45-6.5-4.58-11.08-0.352150000.0120.020.2144 166 255O0III50000.0
56-6.0-4.58-10.58-0.351360000.0110.016.0144 166 255O0IV50000.0
67-5.9-4.58-10.48-0.351240000.0100.015.3144 166 255O0V50000.0
78-5.6-4.58-10.18-0.35940000.060.013.3144 166 255O0VI50000.0
810-9.4-4.43-13.83-0.3527100000.0159.778.8146 168 255O1Ia047600.0
911-6.7-4.43-11.13-0.352250000.0149.322.7146 168 255O1Ia47600.0

Last rows

df_indexAbs MagMvBolo CorrBC(Temp)Bolo MagMbolColor IndexB-VLuminosityLstar/LsunMassMstar/MsunRadiusRstar/RsunStar ColorRGB 0-255StellarTypeTempK
1090122610.2-7.552.65-0.376.9100001.10.00902155 178 255DZ0100000.0
1091122710.8-4.616.19-0.350.2650000.90.00696159 181 255DZ150400.0
1092122811.4-2.678.73-0.270.0255000.80.00864168 189 255DZ225200.0
1093122911.9-1.6010.30-0.200.0060200.70.00944179 197 255DZ316800.0
1094123012.5-0.9011.60-0.130.0018200.60.00922192 207 255DZ412600.0
1095123113.1-0.4512.65-0.070.0006930.50.00890207 218 255DZ510080.0
1096123213.7-0.2013.500.090.0003150.40.00864224 230 255DZ68400.0
1097123314.2-0.0914.110.340.0001800.30.00889243 243 255DZ77200.0
1098123414.8-0.1014.700.550.0001050.20.00887255 247 245DZ86300.0
1099123515.4-0.2215.180.740.0000670.10.00898255 239 225DZ95600.0